Supervised Machine Learning Classifies Inflammatory Bowel Disease Patients by Subtype Using Whole Exome Sequencing Data.

Autor: Stafford IS; Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK.; NIHR Southampton Biomedical Research, University Hospital Southampton, Southampton, UK.; Institute for Life Sciences, University of Southampton, Southampton, UK., Ashton JJ; Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK.; Department of Paediatric Gastroenterology, Southampton Children's Hospital, Southampton, UK., Mossotto E; Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK., Cheng G; Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK.; NIHR Southampton Biomedical Research, University Hospital Southampton, Southampton, UK., Mark Beattie R; Department of Paediatric Gastroenterology, Southampton Children's Hospital, Southampton, UK., Ennis S; Department of Human Genetics and Genomic Medicine, University of Southampton, Southampton, UK.
Jazyk: angličtina
Zdroj: Journal of Crohn's & colitis [J Crohns Colitis] 2023 Nov 08; Vol. 17 (10), pp. 1672-1680.
DOI: 10.1093/ecco-jcc/jjad084
Abstrakt: Background: Inflammatory bowel disease [IBD] is a chronic inflammatory disorder with two main subtypes: Crohn's disease [CD] and ulcerative colitis [UC]. Prompt subtype diagnosis enables the correct treatment to be administered. Using genomic data, we aimed to assess machine learning [ML] to classify patients according to IBD subtype.
Methods: Whole exome sequencing [WES] from paediatric/adult IBD patients was processed using an in-house bioinformatics pipeline. These data were condensed into the per-gene, per-individual genomic burden score, GenePy. Data were split into training and testing datasets [80/20]. Feature selection with a linear support vector classifier, and hyperparameter tuning with Bayesian Optimisation, were performed [training data]. The supervised ML method random forest was utilised to classify patients as CD or UC, using three panels: 1] all available genes; 2] autoimmune genes; 3] 'IBD' genes. ML results were assessed using area under the receiver operating characteristics curve [AUROC], sensitivity, and specificity on the testing dataset.
Results: A total of 906 patients were included in analysis [600 CD, 306 UC]. Training data included 488 patients, balanced according to the minority class of UC. The autoimmune gene panel generated the best performing ML model [AUROC = 0.68], outperforming an IBD gene panel [AUROC = 0.61]. NOD2 was the top gene for discriminating CD and UC, regardless of the gene panel used. Lack of variation in genes with high GenePy scores in CD patients was the best classifier of a diagnosis of UC.
Discussion: We demonstrate promising classification of patients by subtype using random forest and WES data. Focusing on specific subgroups of patients, with larger datasets, may result in better classification.
(© The Author(s) 2023. Published by Oxford University Press on behalf of European Crohn’s and Colitis Organisation.)
Databáze: MEDLINE
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